newsletterJuly 1, 2026·Towards Data Science

Build and Run Your Own AI Agent in the Cloud

Build and deploy an agent on AWS with Strands and AgentCore The post Build and Run Your Own AI Agent in the Cloud appeared first on Towards Data Science.

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•Build and deploy an agent on AWS with Strands and AgentCore The post Build and Run Your Own AI Agent in the Cloud appeared first on Towards Data Science.
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When the Correct Answer Is Nothing, What Does Your Pipeline Return?

The reliability mechanisms we add to LLM pipelines are often the ones that make them confidently wrong. The post When the Correct Answer Is Nothing, What Does Your Pipeline Return? appeared first on Towards Data Science.

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I Trained a Tiny Network to Compress Data. It Drew a Pentagon.

Reproducing Anthropic's "Toy Models of Superposition" from scratch in NumPy, with hand-derived gradients and no borrowed numbers. The post I Trained a Tiny Network to Compress Data. It Drew a Pentagon. appeared first on Towards Data Science.

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From Words to Vectors: What Happens in Between?

A Journey through TF-IDF, vector space, and text classification The post From Words to Vectors: What Happens in Between? appeared first on Towards Data Science.

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How GRPO Trains Small Language Models with Verifiable Rewards

The mechanics behind local reasoning experiments with Unsloth and why the reward function matters as much as the model. The post How GRPO Trains Small Language Models with Verifiable Rewards appeared first on Towards Data Science.

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Build and Run Your Own AI Agent in the Cloud

Key Takeaways

Related Articles

When the Correct Answer Is Nothing, What Does Your Pipeline Return?

I Trained a Tiny Network to Compress Data. It Drew a Pentagon.

From Words to Vectors: What Happens in Between?

How GRPO Trains Small Language Models with Verifiable Rewards

Discussion

Build and Run Your Own AI Agent in the Cloud

Key Takeaways

Related Articles

When the Correct Answer Is Nothing, What Does Your Pipeline Return?

I Trained a Tiny Network to Compress Data. It Drew a Pentagon.

From Words to Vectors: What Happens in Between?

How GRPO Trains Small Language Models with Verifiable Rewards

Discussion

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Towards Data Science
When the Correct Answer Is Nothing, What Does Your Pipeline Return?
The reliability mechanisms we add to LLM pipelines are often the ones that make them confidently wrong. The post When the Correct Answer Is Nothing, What Does Your Pipeline Return? appeared first on Towards Data Science.

Towards Data Science
I Trained a Tiny Network to Compress Data. It Drew a Pentagon.
Reproducing Anthropic's "Toy Models of Superposition" from scratch in NumPy, with hand-derived gradients and no borrowed numbers. The post I Trained a Tiny Network to Compress Data. It Drew a Pentagon. appeared first on Towards Data Science.

Towards Data Science
From Words to Vectors: What Happens in Between?
A Journey through TF-IDF, vector space, and text classification The post From Words to Vectors: What Happens in Between? appeared first on Towards Data Science.

Towards Data Science
How GRPO Trains Small Language Models with Verifiable Rewards
The mechanics behind local reasoning experiments with Unsloth and why the reward function matters as much as the model. The post How GRPO Trains Small Language Models with Verifiable Rewards appeared first on Towards Data Science.